'GaZoGaBu'
In Asimovs stories, the name of the robot Daneel Olivaw is similar to Daniel Oliver. It also means we can allow more short, 3 letter prefixes. fills a structure with randomized data to mock a service or other model provider. Generates an english first name based on gender. Skip to 23 minutes in if you want to see what the library can do.]. For example assuming the random index is = 2, word = '' + 'Zo' (empty string with 'Zo' added at the end). 'BuBuGa' Note - in python, instead of random.randint() you can use random.choice() to the same effect: What about adding several syllables to a word? Thats ok in this case, as my goal was to learn something about neural networks.
As I've mentioned above, with our small set of Shadok syllables and word length restrictions, we can generate around 2000 different words. 'GaMeuBuMeu' I had hoped to use the Web Speech API but it seems pretty limited at this point. We run it several times and we get words of varying lengths: 'GaBuZoGaMeu' Whether you like it or not, people do make snap judgements and form first impressions. using corrupted Latin such as 'Lorem ipsum') to present templates, apps and websites. We'll also introduce an arbitrary boundary to how many attempts we make. I plan to write a more detailed devlog post about the procedural 3D environment soon. It demonstrates the markov-namegen haxelib. It causes the program to get slower and slower as it gets harder and harder to find the remaining unique words. Sort by Damerau-Levenshtein distance to list results by similarity. In our case we just want a set of funny-looking words, and we want to be fast and efficient about it (i.e. If the word is too short, we override the requested number of syllables to add more.
With this generator you can generate names for: - pets - planets - your children - people - cities - lands - Linux distributions - and anything you want
Moreover, you can generate custom Lorem Ipsums! I started this mini-project primarily to play with neural networks.
I then read about recurrent[9] neural networks {1}[10] - {2}[11] - {3}[12], which are about sequences of inputs and outputs. But there is a problem. At the end of the fifth run, count_syllables = 5. From March 22, 2021, if your country of residence is Japan, it will be displayed as "tax included". For instance I want to generate player names and I want them to be neither too short, nor too long. We're currently developing a cool app based on our site. What happens if I append a prefix or suffix[20], D IH M AY K AH L or M AY K AH L S AH N? This means they need to store everything in memory. In 2018 I played more with alignment in a project to procedurally modify spelling, and I made an interactive demo where you can put in your own words. There are dozens of training presets, and the corpus can be manually edited through the "Settings" dropdown section above. M AY K AH L produces michel. 'MeuBuGaMeuMeu' - 5 syllables - 13 characters - too long. Purchase Avoyd All the other words will also have a lot more 'Meu' syllables included: 'MeuBuGaMeuMeu' GaBu is the prefix, 2365 is the suffix. To work around this let's calculate the min and max number of syllables. People are becoming more and more aware of identity theft and the need to keep your identity private in some situations. Creates mongoose collections of first and last names and generates random full names. 'GaZoBuGaGaZo' If you've already narrowed down your requirements, you can specify the kind of names that you want to see, but you can also start with random names and see where it leads. When I wanted the system to say daneel, D AE N IY1 L is the closest match, but Im working with phonemes D AE N IY L which sound a bit different. You can specify male names, female names or both. 'MeuGaZoGaMeu' How to Set up Github Actions for CI/CD in React Apps. I downloaded their g2p-seq2seq code[19]. days Names matter. 'ZoZoMeuMeuBu' In these conditions: 'Zo' - 1 syllable - 2 characters - too short Some of our tools actually invent names, generating examples that are completely unique. So instead I read the g2p-seq2seq code and modified it to work on phonemes to graphemes (phoneme2grapheme.py). Great! Shadok syllables are 2 or 3 characters long. and move all the code into the function 'generate()'. M AY K AH L produces michael. I then tried using the model: I typed in names like michael and got back pronunciations like M IH CH EY L (pronouncing the ch like cheese and not like kite).
'Meu' - 1 syllable - 3 characters - ok lengthwise Now, I can get 50 teraFLOPs for $10/hour. 'GaMeuMeuZoGa' The "Similar To" parameter allows you to sort the generated names by their similarity to the name that you enter. Can I generate names that are similar to existing names, but with minor changes? In other words count_syllables = MAX_SYLLABLES hence the loop start condition (count_syllables < MAX_SYLLABLES) is no longer valid and the loop ends. * From April 1, 2021, Japanese law "mandatory total amount display", Please use the link below for tax-excluded display, [ PUBLISHER SPOTLIGHT ] From 8:00 on July 12, 2022 to 23:59:59 on July 25 (PDT), until finish Random name generators are useful for developers and designers who wish to avoid 'greeking' (e.g. 2. We still use a set (why a set?) This is the method I've used for generating default usernames in our enkiWS system (simplified, I'll explain the other trick I used further down). Example: we use enkiWS to power our website www.enkisoftware.com. The name generator was written using Haxe. For spelling I looked for projects that convert phonemes to graphemes but I didnt find anything. You can either generate random names or guide the process. People who want to personalise their name can replace the prefix with a string of their choice (between 3 and 12 characters). GaBu#2365 I remember watching it as a child. The neural network may be good at capturing English spelling rules for existing words, but I think alignment would offer more possibilities for the designer or procedural generator to control how generated names work. What happens if I change the first I sound to M EH K AH L or M UH K AH L? 
A great side-effect of writing this detailed tutorial is that I found out my Shadok name generator didn't work properly and I could also simplify it. The first step was to clean up the CMU dictionary. Some generated content parodies existing styles and artists, whilst others are based on original structures. Here are my first 10 results when using the settings above: Here is the demo in action. Lexic is an extensible, customizable, procedural name generator.
'BuBu' The "Start", "End", "Includes", "Excludes" and "Regex" options are used to filter the generated words. If you're interested in the procedural generation in our game Avoyd, this article in Seeds gives an overview of how we built the 3D 'boxes in space'.
You can find names for characters and babies from different backgrounds including searching by country, religion and name popularity by birth year. Run these on Mac: Note that the [[inpt PHON]] syntax doesnt work with the voices added in newer version of Mac OS, according to this stackexchange question[30]. [1]:http://neuralnetworksanddeeplearning.com/, [2]:http://karpathy.github.io/2015/05/21/rnn-effectiveness/, [4]:https://en.wikipedia.org/wiki/Convolutional_neural_network, [5]:http://neuralnetworksanddeeplearning.com/chap6.html#introducing_convolutional_networks, [6]:http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/, [7]:https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/, [8]:https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721, [9]:https://en.wikipedia.org/wiki/Recurrent_neural_network, [10]:http://distill.pub/2016/augmented-rnns/, [11]:https://twitter.com/hardmaru/status/799863548893544452, [12]:http://colah.github.io/posts/2015-08-Understanding-LSTMs/, [13]:https://www.tensorflow.org/versions/r0.11/tutorials/mnist/beginners/, [14]:https://www.ssa.gov/OACT/babynames/limits.html, [15]:https://www.tensorflow.org/versions/r0.11/tutorials/seq2seq/index.html#sequence-to-sequence-basics, [16]:https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa, [17]:https://research.googleblog.com/2016/09/a-neural-network-for-machine.html, [18]:http://www.speech.cs.cmu.edu/cgi-bin/cmudict, [19]:https://github.com/cmusphinx/g2p-seq2seq, [20]:https://en.wikipedia.org/wiki/List_of_family_name_affixes, [21]:https://www.youtube.com/watch?v=yJ6iN5M42sA&disable_polymer=1, [22]:https://pincelate.readthedocs.io/en/latest/, [23]:https://en.wikipedia.org/wiki/International_Phonetic_Alphabet, [24]:https://en.wikipedia.org/wiki/Arpabet, [25]:https://developer.apple.com/library/content/documentation/UserExperience/Conceptual/SpeechSynthesisProgrammingGuide/Phonemes/Phonemes.html, [26]:https://en.wikipedia.org/wiki/Speech_Synthesis_Markup_Language, [27]:http://www.cstr.ed.ac.uk/projects/festival/, [28]:https://en.wikipedia.org/wiki/SABLE, [29]:http://people.ds.cam.ac.uk/ssb22/gradint/lexconvert.py, [30]:https://superuser.com/questions/814510/how-can-you-make-os-xs-say-command-speak-ipa-characters, [31]:https://www.youtube.com/watch?v=UXW6Cs82UKo, [32]:https://github.com/tensorflow/tensorflow/issues/654, [33]:https://github.com/cmusphinx/g2p-seq2seq/issues/54, [34]:https://github.com/hunkim/word-rnn-tensorflow/issues/28, [35]:http://www.aclweb.org/anthology/P10-1080, [36]:https://www.ssa.gov/OACT/babynames/limits.html. When you're naming characters, you get a chance to bundle a huge amount of information into a very short phrase, by relying on readers' and viewers' preconceptions. To use the generator as part of a registration system, you will need to store the list of unique generated words / usernames in a permanent repository, for instance a table in a database. Its not using the pronunciation at all; its just predicting what letters fit together. You can get data: MNIST for handwriting digit recognition, CMUdict for pronunciation, web archives, image databases, wikipedia, imdb, open street map, and lots more. seconds. If I feed those spellings back into Apples speech synthesis, I dont get the pronunciations I was trying to produce, so in that sense, even though the spellings are reasonable, they may not match what I want people to hear in their heads when they read the words. Markov Namegen procedurally generates names with a Markov process. The aim of our name generator is to help you find the perfect name for any occasion. The easiest way is to use a set (an unordered collection of unique elements in python). Press the "Settings" button to show advanced options. For game design, including procedural generation, I think I want the designer to have more of a say in what comes out, and for that I will continue to use simpler systems unless theres some compelling reason to use neural networks. Markov Namegen is a Markov chain-based procedural name generator library and demo website written in Haxe. However enkiWS supports non-unique display names by appending 4 digits between 1000 and 9999 to the end of every name. The tools are designed to be cool and entertain, but also help aspiring writers create a range of different media, including plots, lyrics for songs, poems, letters and names. LibHunt tracks mentions of software libraries on relevant social networks. Search at random or filter and sort by gender, popularity, birth year, country, personality and many other interesting properties. As you increase the "Order" setting, larger chunks of the training words will appear in the generated output. Fortunately its actually making checkpoints along the way. I usually need a mini project to learn a topic, so I decided Id use procedural name generation to guide my learning. This restricts the number of words we can generate to around 330. 